As discussed in Section REF , for Ours-Ens we train five GCNNs at each iteration while we train only one Ours-MCD. In both cases, after each training iteration, we use the genetic algorithm NSGAII [1]} coupled to the GCNN(s) to sample 30 new airfoil shapes and to evaluate the corresponding acquisition function. For Kriging, we proceed similarly but use GPs instead of a GCNN to run the generic algorithm and evaluate the acquisition function. For GCNN, we use the network to predict lift-to-drag values \(R(\widetilde{\textbf {y}}_{i})\)  and pick sampled shapes for simulations according to this metric instead of the acquisition function, which would require an uncertainty estimate.
